A new wipe transition detection approach was proposed. By analyzing the spatial-temporal characteristics of an ideal wipe production model, the concept of wipe transition strip (TS) was introduced. The macroblock type...A new wipe transition detection approach was proposed. By analyzing the spatial-temporal characteristics of an ideal wipe production model, the concept of wipe transition strip (TS) was introduced. The macroblock type information of P-frames is used to extract TS regions. An improved TS region accumulation technique is performed for detecting and verifying wipe transitions. The experimental results indicate that the proposed approach is capable of detecting various wipe transitions quickly and accurately.展开更多
This paper proposes an optical fiber evanescent wave sensor for phase transition detection of organic compounds,which was validated using n-octadecane.The sensor is constructed by arc-discharge splicing single-mode fi...This paper proposes an optical fiber evanescent wave sensor for phase transition detection of organic compounds,which was validated using n-octadecane.The sensor is constructed by arc-discharge splicing single-mode fiber(SMF)into a waist-enlarged fusion taper(WEFT)structure using a fiber fusion splicer.When two WEFTs are connected in series,they form a Mach–Zehnder interferometer(MZI).Since n-octadecane has different refractive indices in its solid and liquid states during the phase change,the change in refractive index causes variation in the interference dips in the spectrum,enabling the distinction between the solid and liquid states.However,traditional wavelength and intensity tracking methods require precise numerical analysis,limiting their practical applications.Therefore,we propose using machine learning to assist the WEFT structure in phase change detection.During the heating and cooling processes,the K-means algorithm is first applied to classify the solid and liquid states,corresponding to the two phases of the transition.Subsequently,a Gaussian mixture model(GMM)is used for optimization,allowing for accurate differentiation between the liquid and solid states of n-octadecane.The results show that during the heating and cooling processes,after training on the spectral data,the average silhouette coefficients were 0.8619 and 0.8813,respectively,and the log-likelihood values were-21.8062 and-1.175.The sensor we propose has a simple structure and is easy to manufacture.Combined with machine learning algorithms,it holds great potential for application in the field of phase change energy storage.展开更多
Dynamic optimization relies on runtime profile information to improve the performance of program execution. Traditional profiling techniques incur significant overhead and are not suitable for dynamic optimization. In...Dynamic optimization relies on runtime profile information to improve the performance of program execution. Traditional profiling techniques incur significant overhead and are not suitable for dynamic optimization. In this paper, a new profiling technique is proposed, that incorporates the strength of both software and hardware to achieve near-zero overhead profiling. The compiler passes profiling requests as a few bits of information in branch instructions to the hardware, and the processor executes profiling operations asynchronously in available free slots or on dedicated hardware. The compiler instrumentation of this technique is implemented using an Itanium research compiler. The result shows that the accurate block profiling incurs very little overhead to the user program in terms of the program scheduling cycles. For example, the average overhead is 0.6% for the SPECint95 benchmarks. The hardware support required for the new profiling is practical. The technique is extended to collect edge profiles for continuous phase transition detection. It is believed that the hardware-software collaborative scheme will enable many profile-driven dynamic optimizations for EPIC processors such as the Itanium processors.展开更多
In this paper,a two-layer hierarchical structure of optimization and control for polypropylene grade transition was raised to overcome process uncertain disturbances that led to the large deviation between the open-lo...In this paper,a two-layer hierarchical structure of optimization and control for polypropylene grade transition was raised to overcome process uncertain disturbances that led to the large deviation between the open-loop reference trajectory and the actual process.In the upper layer,the variant time scale based control vector parametric methods(VTS-CVP) was used for dynamic optimization of transition reference trajectory,while nonlinear model predictive controller(NMPC) based on closed-loop subspace and piece-wise linear(SSARX-PWL) model in the lower layer was tracking to the reference trajectory from the upper layer for overcoming high-frequency disturbances.Besides,mechanism about trajectory deviation detection and optimal trajectory updating online were introduced to ensure a smooth transition for the entire process.The proposed method was validated with the real data from an industrial double-loop propylene polymerization reaction process with developed dynamic mechanism mathematical model.展开更多
文摘A new wipe transition detection approach was proposed. By analyzing the spatial-temporal characteristics of an ideal wipe production model, the concept of wipe transition strip (TS) was introduced. The macroblock type information of P-frames is used to extract TS regions. An improved TS region accumulation technique is performed for detecting and verifying wipe transitions. The experimental results indicate that the proposed approach is capable of detecting various wipe transitions quickly and accurately.
文摘This paper proposes an optical fiber evanescent wave sensor for phase transition detection of organic compounds,which was validated using n-octadecane.The sensor is constructed by arc-discharge splicing single-mode fiber(SMF)into a waist-enlarged fusion taper(WEFT)structure using a fiber fusion splicer.When two WEFTs are connected in series,they form a Mach–Zehnder interferometer(MZI).Since n-octadecane has different refractive indices in its solid and liquid states during the phase change,the change in refractive index causes variation in the interference dips in the spectrum,enabling the distinction between the solid and liquid states.However,traditional wavelength and intensity tracking methods require precise numerical analysis,limiting their practical applications.Therefore,we propose using machine learning to assist the WEFT structure in phase change detection.During the heating and cooling processes,the K-means algorithm is first applied to classify the solid and liquid states,corresponding to the two phases of the transition.Subsequently,a Gaussian mixture model(GMM)is used for optimization,allowing for accurate differentiation between the liquid and solid states of n-octadecane.The results show that during the heating and cooling processes,after training on the spectral data,the average silhouette coefficients were 0.8619 and 0.8813,respectively,and the log-likelihood values were-21.8062 and-1.175.The sensor we propose has a simple structure and is easy to manufacture.Combined with machine learning algorithms,it holds great potential for application in the field of phase change energy storage.
文摘Dynamic optimization relies on runtime profile information to improve the performance of program execution. Traditional profiling techniques incur significant overhead and are not suitable for dynamic optimization. In this paper, a new profiling technique is proposed, that incorporates the strength of both software and hardware to achieve near-zero overhead profiling. The compiler passes profiling requests as a few bits of information in branch instructions to the hardware, and the processor executes profiling operations asynchronously in available free slots or on dedicated hardware. The compiler instrumentation of this technique is implemented using an Itanium research compiler. The result shows that the accurate block profiling incurs very little overhead to the user program in terms of the program scheduling cycles. For example, the average overhead is 0.6% for the SPECint95 benchmarks. The hardware support required for the new profiling is practical. The technique is extended to collect edge profiles for continuous phase transition detection. It is believed that the hardware-software collaborative scheme will enable many profile-driven dynamic optimizations for EPIC processors such as the Itanium processors.
基金Supported by the Electronic Information Industry Development Foundation of China(20140806)the National Natural Science Foundation of China(61374121,61134007)
文摘In this paper,a two-layer hierarchical structure of optimization and control for polypropylene grade transition was raised to overcome process uncertain disturbances that led to the large deviation between the open-loop reference trajectory and the actual process.In the upper layer,the variant time scale based control vector parametric methods(VTS-CVP) was used for dynamic optimization of transition reference trajectory,while nonlinear model predictive controller(NMPC) based on closed-loop subspace and piece-wise linear(SSARX-PWL) model in the lower layer was tracking to the reference trajectory from the upper layer for overcoming high-frequency disturbances.Besides,mechanism about trajectory deviation detection and optimal trajectory updating online were introduced to ensure a smooth transition for the entire process.The proposed method was validated with the real data from an industrial double-loop propylene polymerization reaction process with developed dynamic mechanism mathematical model.
